A recursive least squares implementation for adaptive beamforming under quadratic constraint

被引：8

作者：

Tian, Z ^{[1
]}

Bell, KL ^{[1
]}

Van Trees, HL ^{[1
]}

机构：

[1] George Mason Univ, Ctr Excellence C3I, Fairfax, VA 22030 USA

来源：

NINTH IEEE SIGNAL PROCESSING WORKSHOP ON STATISTICAL SIGNAL AND ARRAY PROCESSING, PROCEEDINGS | 1998年

关键词：

D O I：

10.1109/SSAP.1998.739321

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Quadratic constraints on the weight vector of an adaptive linearly constrained minimum power (LCMP) beamformer can improve robustness to pointing errors and to random perturbations in sensor parameters. In this paper, we propose a technique for implementing a quadratic inequality constraint with recursive least squares (RLS) updating. A variable diagonal loading term is added at each step, where the amount of loading is found from the solution to a quadratic equation. Simulations under different scenarios demonstrate that this algorithm outperforms both the RLS beamformer with no quadratic constraint, and the RLS beamformer using the scaled projection technique.

引用

页码：9 / 12

页数：4

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